Margie and Bill Klesse College of Engineering and Integrated Design
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Browsing Margie and Bill Klesse College of Engineering and Integrated Design by Author "Ahmed, Sara"
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Item Implementing Very-Short-Term Forecasting of Residential Load Demand Using a Deep Neural Network Architecture(2023-04-23) Gonzalez, Reynaldo; Ahmed, Sara; Alamaniotis, MiltiadisThe need for and interest in very-short-term load forecasting (VSTLF) is increasing and important for goals such as energy pricing markets. There is greater challenge in predicting load consumption for residential-load-type data, which is highly variable in nature and does not form visible patterns present in aggregated nodal-type load data. Previous works have used methods such as LSTM and CNN for VSTLF; however, the use of DNN has yet to be investigated. Furthermore, DNNs have been effectively used in STLF but have not been applied to very-short-term time frames. In this work, a deep network architecture is proposed and applied to very-short-term forecasting of residential load patterns that exhibit high variability and abrupt changes. The method extends previous work by including delayed load demand as an input, as well as working for 1 min data resolution. The deep model is trained on the load demand data of selected days—one, two, and a week—prior to the targeted day. Test results on real-world residential load patterns encompassing a set of 32 days (a sample from different seasons and special days) exhibit the efficiency of the deep network in providing high-accuracy residential forecasts, as measured with three different error metrics, namely MSE, RMSE, and MAPE. On average, MSE and RMSE are lower than 0.51 kW and 0.69 kW, and MAPE lower than 0.51%.Item Roadway Embedded Smart Illumination Charging System for Electric Vehicles(2023-01-11) Fernandez, Daniel; Sebastian, Ann; Raby, Patience; Genedy, Moneeb; Ahn, Ethan C.; Taha, Mahmoud M. Reda; Dessouky, Samer; Ahmed, SaraInspired by the fact that there is an immense amount of renewable energy sources available on the roadways, such as mechanical pressure, this study presents the development and implementation of an innovative charging technique for electric vehicles (EVs) by fully utilizing the existing roadways and state-of-the-art nanotechnology and power electronics. The developed Smart Illuminative Charging is a novel wireless charging system that uses LEDs powered by piezoelectric materials as the energy transmitter source and thin film solar panels placed at the bottom of the EVs as the receiver, which is then poised to deliver the harvested energy to the vehicle's battery. The piezoelectric materials were tested for their mechanical-to-electrical energy conversion capabilities and the relatively large-area EH2N samples (2 cm × 2 cm) produced high output voltages of up to 52 mV upon mechanical pressure. Furthermore, a lab-scale prototype device was developed to testify the proposed mechanism of illuminative charging (i.e., "light" coupled pavement and vehicle as a wireless energy transfer medium).